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from utils_nms import apply_nms
from PIL import Image
import cv2
import torch
import numpy as np
import copy
import logging
def compute_boxes_and_sizes(PRED_DOWNSCALE_FACTORS, GAMMA,
NUM_BOXES_PER_SCALE):
BOX_SIZE_BINS = [1]
g_idx = 0
while len(BOX_SIZE_BINS) < NUM_BOXES_PER_SCALE * len(
PRED_DOWNSCALE_FACTORS):
gamma_idx = len(BOX_SIZE_BINS) // (len(GAMMA) - 1)
box_size = BOX_SIZE_BINS[g_idx] + GAMMA[gamma_idx]
BOX_SIZE_BINS.append(box_size)
g_idx += 1
BOX_SIZE_BINS_NPY = np.array(BOX_SIZE_BINS)
BOXES = np.reshape(BOX_SIZE_BINS_NPY, (4, 3))
BOXES = BOXES[::-1]
return BOXES, BOX_SIZE_BINS
def upsample_single(input_, factor=2):
channels = input_.size(1)
indices = torch.nonzero(input_)
indices_up = indices.clone()
# Corner case!
if indices_up.size(0) == 0:
return torch.zeros(input_.size(0),input_.size(1), input_.size(2)*factor, input_.size(3)*factor).cuda()
# return torch.zeros(input_.size(0), input_.size(1),
# input_.size(2) * factor, input_.size(3) * factor)
indices_up[:, 2] *= factor
indices_up[:, 3] *= factor
output = torch.zeros(input_.size(0),input_.size(1), input_.size(2)*factor, input_.size(3)*factor).cuda()
# output = torch.zeros(input_.size(0), input_.size(1),
# input_.size(2) * factor, input_.size(3) * factor)
output[indices_up[:, 0], indices_up[:, 1], indices_up[:, 2], indices_up[:,
3]] = input_[
indices[:, 0], indices[:, 1], indices[:, 2], indices[:, 3]]
output[indices_up[:, 0], channels - 1, indices_up[:, 2] + 1, indices_up[:,
3]] = 1.0
output[indices_up[:, 0], channels - 1, indices_up[:, 2], indices_up[:,
3] + 1] = 1.0
output[indices_up[:, 0], channels - 1, indices_up[:, 2] + 1, indices_up[:,
3] + 1] = 1.0
# output_check = nn.functional.max_pool2d(output, kernel_size=2)
return output
def get_upsample_output(model_output, output_downscale):
upsample_max = int(np.log2(16 // output_downscale))
upsample_pred = []
for idx, out in enumerate(model_output):
out = torch.nn.functional.softmax(out, dim=1)
upsample_out = out
for n in range(upsample_max - idx):
upsample_out = upsample_single(upsample_out, factor=2)
upsample_pred.append(upsample_out.cpu().data.numpy().squeeze(0))
return upsample_pred
def box_NMS(predictions, nms_thresh, BOXES):
Scores = []
Boxes = []
for k in range(len(BOXES)):
scores = np.max(predictions[k], axis=0)
boxes = np.argmax(predictions[k], axis=0)
# index the boxes with BOXES to get h_map and w_map (both are the same for us)
mask = (boxes < 3) # removing Z
boxes = (boxes + 1) * mask
scores = (
scores * mask) # + 100 # added 100 since we take logsoftmax and it's negative!!
boxes = (boxes == 1) * BOXES[k][0] + (boxes == 2) * BOXES[k][1] + (
boxes == 3) * BOXES[k][2]
Scores.append(scores)
Boxes.append(boxes)
x, y, h, w, scores = apply_nms(Scores, Boxes, Boxes, 0.5,
thresh=nms_thresh)
nms_out = np.zeros((predictions[0].shape[1], predictions[0].shape[
2])) # since predictions[0] is of size 4 x H x W
box_out = np.zeros((predictions[0].shape[1], predictions[0].shape[
2])) # since predictions[0] is of size 4 x H x W
for (xx, yy, hh) in zip(x, y, h):
nms_out[yy, xx] = 1
box_out[yy, xx] = hh
assert (np.count_nonzero(nms_out) == len(x))
return nms_out, box_out
def get_box_and_dot_maps(pred, nms_thresh, BOXES):
assert (len(pred) == 4)
# NMS on the multi-scale outputs
nms_out, h = box_NMS(pred, nms_thresh, BOXES)
return nms_out, h
def get_boxed_img(image, original_emoji, h_map, w_map, gt_pred_map,
prediction_downscale, BOXES, BOX_SIZE_BINS,
thickness=1, multi_colours=False):
if image.shape[2] != 3:
boxed_img = image.astype(np.uint8).transpose((1, 2, 0)).copy()
else:
boxed_img = image.astype(np.uint8).copy()
head_idx = np.where(gt_pred_map > 0)
H, W = boxed_img.shape[:2]
Y, X = head_idx[-2], head_idx[-1]
# scale to image
enlarge_factor = max(((H * W) / (48 ** 2)) // 300, 1)
for i, (y, x) in enumerate(zip(Y, X)):
h, w = h_map[y, x] * prediction_downscale, w_map[
y, x] * prediction_downscale
scale = ((BOX_SIZE_BINS.index(h // prediction_downscale)) // 3) + 1
if enlarge_factor > 1:
h *= enlarge_factor / 2
w *= enlarge_factor / 2
expand_w = (0.2 * scale * w) // 2
expand_h = (0.2 * scale * h) // 2
y2 = min(int((prediction_downscale * x + w / 2) + expand_w), W)
y1 = max(int((prediction_downscale * x - w / 2) - expand_w), 0)
x2 = min(int((prediction_downscale * y + h / 2) + expand_h), H)
x1 = max(int((prediction_downscale * y - h / 2) - expand_h), 0)
emoji = copy.deepcopy(original_emoji)
# emoji = original_emoji.copy()
width = x2 - x1
height = y2 - y1
emoji = cv2.resize(emoji, (height, width))
# emoji = emoji.resize((width, height))
# emoji = np.array(emoji)
# https://gist.github.com/clungzta/b4bbb3e2aa0490b0cfcbc042184b0b4e
# Extract the alpha mask of the RGBA image, convert to RGB
r, g, b, a = cv2.split(emoji)
overlay_color = cv2.merge((b, g, r))
# Apply some simple filtering to remove edge noise
mask = cv2.medianBlur(a, 5)
mask[mask != 255] = 0
roi = boxed_img[x1:x2, y1:y2]
# Black-out the area behind the emoji in our original ROI
img1_bg = cv2.bitwise_and(roi.copy(), roi.copy(),
mask=cv2.bitwise_not(mask))
# Mask out the emoji from the emoji image.
img2_fg = cv2.bitwise_and(overlay_color, overlay_color, mask=mask)
# Update the original image with our new ROI
boxed_img[x1:x2, y1:y2] = cv2.add(img1_bg, img2_fg)
return boxed_img |